The proposed attention consistency works effectively in accompany with a remoulded contrastive loss, where additional within-modal information is considered.
Different from AVID [1]}, we only involve within-modal negative samples from previous batch data, thereby without extra memory cost for resampling within-modal positive samples for the current batch.
This simple yet effective modification successfully boosts the performance.
Experiments on various downstream tasks including action recognition, video retrieval, and audio classification show that CMAC can improve the state-of-the-art results.
